elthio-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the core behavior (checking interactions and generating a schedule) but omits details about the output format, handling of missing times, or how conflicts are reported. It is accurate but shallow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core function and a clear usage case. There is no filler; every clause adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having six parameters and producing a complex schedule, the description does not explain the schedule output, the optionality of time parameters, or how missing times are handled. With no output schema, this is a significant gap for an agent to correctly invoke and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, meaning every parameter already has an explanation in the schema. The description adds no additional parameter semantics, so the baseline of 3 applies. It does not clarify relationships between time parameters or how they influence the schedule.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('check') and resource ('complete supplement and medication stack'), and clearly outputs a 'full daily timing schedule.' It also explicitly contrasts with checking individual items, which distinguishes it from siblings like check_supplement_safety and get_supplement_timing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides a clear when-to-use directive ('when a user wants to check their entire supplement regimen or build a complete daily schedule'), but does not explicitly state when NOT to use it or name alternative tools. The sibling names are present in the context, but the description itself does not contrast with them explicitly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden. It openly lists the kinds of results returned (interaction data, safety score, quality grade, filler detection, NIH verification, timing recommendation), which gives the agent a clear behavioral model. It does not reveal any limitations or underlying data-source behavior, but the read-only 'check' framing is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the core purpose comes first, followed by return categories and a usage trigger. Every sentence adds value, though the second sentence is a dense list of outputs that could arguably be trimmed or structured better.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so listing the return categories is important and mostly sufficient. However, the description bundles outputs that appear to belong to sibling tools (quality grade, NIH verification, timing) without clarifying how this tool relates to them or when to call those siblings instead. This creates moderate ambiguity for an agent navigating the full toolset.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters adequately. The description adds little to parameter semantics beyond echoing the concept of 'existing supplements' and medications, which matches the schema. This is the baseline 3 case where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check'), a clear resource (supplement safety against medications and existing supplements), and lists the outputs returned. It does not explicitly differentiate itself from sibling tools like grade_supplement_quality or get_supplement_timing, even though its output list overlaps with them, so it stops just short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear trigger conditions: 'Use this whenever a user asks about supplement safety, drug interactions, or whether they can take a supplement with their medications.' It does not state when not to use it or point to alternatives such as verify_nih_label or grade_supplement_quality, which are relevant given the overlapping outputs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It transparently states that the tool returns a slot, clinical reasoning, and personalized instruction, making it clear this is a read-only advisory operation with no side effects. It does not mention data sources or limitations, but for this query-oriented tool the disclosed behavior is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences convey the tool's purpose, what it returns, and when to use it, with no filler. The primary action and outputs are front-loaded, and the usage guidance follows naturally. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity tool with two parameters, no output schema, and no annotations, the description clearly explains return values (slot, reasoning, instruction) and the trigger scenario. An agent has enough to decide when to call it and what to expect back, even without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters: 'supplement' with an example and 'medications' as affecting timing. The description adds only that the result includes 'personalized instruction,' which indirectly implies the role of medications. The schema does the heavy lifting, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Get the optimal time to take a supplement and why'), identifies the resource (a supplement), and enumerates the concrete return components (slot, clinical reasoning, personalized instruction). This clearly separates it from sibling tools focused on safety, quality, label verification, and full-stack checks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'when a user asks when to take a supplement or how to build a timing schedule.' This gives clear context, though it does not name sibling alternatives or state when not to use it. The guidance is sufficient for most agent routing decisions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the responsibility for behavioral disclosure. It communicates the read-only operation via 'Get', describes the output scale (A-D), and names the evaluation criteria. It does not mention dependencies, failure modes, or how the optional other_ingredients parameter affects grading, leaving some behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler, and the core function is front-loaded in the first phrase. Every clause adds decision-relevant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a simple 2-param getter, but with no output schema and no annotations the description must cover invocation semantics. It explains output format and typical use cases, yet it leaves ambiguity about brand comparisons (single call vs. multiple calls) and the effect of omitting other_ingredients on filler detection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description mentions filler detection and NIH verification, but it does not add parameter-level detail beyond what the schema already provides; the examples and optionality are already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Get'), resource ('quality grade (A-D) for a supplement'), and the three evaluation criteria (bioavailability, filler detection, NIH verification). It also lists concrete trigger questions, making its purpose clear. However, it does not explicitly distinguish itself from sibling tools such as verify_nih_label or check_supplement_safety, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: 'when a user asks about supplement quality, which form is best, whether a supplement has concerning fillers, or wants to compare brands.' This gives clear context for selection, but it does not state when to prefer a sibling tool or when not to use this tool, so it lacks exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the disclosure burden. It usefully discloses that the tool 'Returns verification status, DSLD ID, and confidence level', but does not describe behavior for edge cases (e.g. no match found) or any data-source semantics beyond matching.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences: purpose, return values, and usage context, all front-loaded with no filler. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with full schema coverage, the description is complete: it covers what it does, what it returns, and when to use it. Minor gap is the absence of edge-case or 'no record found' behavior, which would normally be covered by an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single 'supplement' parameter is already described as the name to verify. The description adds no extra meaning beyond the schema, matching the baseline for full coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Verify') and resource ('NIH Dietary Supplement Label Database'), and names the output briefly. The sibling tools cover safety, timing, and quality, so this verification-against-a-database purpose is clearly distinct from them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this when a user asks if a supplement is legitimate or wants to verify label accuracy.' It provides clear when-to-use context, though it does not state exclusions or name an alternative tool as a fallback.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.